Overview

Dataset statistics

Number of variables17
Number of observations3082
Missing cells11283
Missing cells (%)21.5%
Duplicate rows511
Duplicate rows (%)16.6%
Total size in memory409.5 KiB
Average record size in memory136.0 B

Variable types

Numeric5
Categorical9
DateTime2
Unsupported1

Alerts

Dataset has 511 (16.6%) duplicate rowsDuplicates
_embedded.show.url has a high cardinality: 633 distinct values High cardinality
_embedded.show.name has a high cardinality: 631 distinct values High cardinality
_embedded.show.genres has a high cardinality: 160 distinct values High cardinality
_embedded.show.officialSite has a high cardinality: 570 distinct values High cardinality
_embedded.show.runtime is highly correlated with _embedded.show.id and 5 other fieldsHigh correlation
_embedded.show.averageRuntime is highly correlated with _embedded.show.type and 4 other fieldsHigh correlation
_embedded.show.type is highly correlated with _embedded.show.id and 6 other fieldsHigh correlation
_embedded.show.schedule.time is highly correlated with _embedded.show.id and 8 other fieldsHigh correlation
_embedded.show.status is highly correlated with _embedded.show.type and 4 other fieldsHigh correlation
_embedded.show.language is highly correlated with _embedded.show.id and 8 other fieldsHigh correlation
_embedded.show.id is highly correlated with _embedded.show.type and 6 other fieldsHigh correlation
_embedded.show.schedule.days is highly correlated with _embedded.show.id and 8 other fieldsHigh correlation
_embedded.show.rating.average is highly correlated with _embedded.show.id and 5 other fieldsHigh correlation
_embedded.show.weight is highly correlated with _embedded.show.id and 4 other fieldsHigh correlation
_embedded.show.language has 34 (1.1%) missing values Missing
_embedded.show.runtime has 1017 (33.0%) missing values Missing
_embedded.show.ended has 1699 (55.1%) missing values Missing
_embedded.show.officialSite has 408 (13.2%) missing values Missing
_embedded.show.schedule.time has 2213 (71.8%) missing values Missing
_embedded.show.rating.average has 2661 (86.3%) missing values Missing
_embedded.show.network has 3082 (100.0%) missing values Missing
_embedded.show.averageRuntime has 169 (5.5%) missing values Missing
_embedded.show.network is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-09-17 21:54:02.868058
Analysis finished2022-09-17 21:55:03.305934
Duration1 minute and 0.44 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

_embedded.show.id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct633
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46952.47664
Minimum802
Maximum64152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T21:55:03.436527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum802
5-th percentile15250
Q144135
median51954
Q352806
95-th percentile60156
Maximum64152
Range63350
Interquartile range (IQR)8671

Descriptive statistics

Standard deviation12449.65797
Coefficient of variation (CV)0.2651544468
Kurtosis3.026877779
Mean46952.47664
Median Absolute Deviation (MAD)2825
Skewness-1.820832003
Sum144707533
Variance154993983.5
MonotonicityNot monotonic
2022-09-17T21:55:03.663840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1525038
 
1.2%
5535532
 
1.0%
3060631
 
1.0%
5274330
 
1.0%
5210429
 
0.9%
5242128
 
0.9%
4791226
 
0.8%
5280626
 
0.8%
5476224
 
0.8%
5075224
 
0.8%
Other values (623)2794
90.7%
ValueCountFrequency (%)
8024
 
0.1%
15962
 
0.1%
18255
 
0.2%
22666
 
0.2%
250419
0.6%
28551
 
< 0.1%
37341
 
< 0.1%
40913
 
0.1%
50581
 
< 0.1%
60904
 
0.1%
ValueCountFrequency (%)
641521
 
< 0.1%
641324
 
0.1%
640238
0.3%
639675
0.2%
637613
 
0.1%
6371910
0.3%
633104
 
0.1%
631554
 
0.1%
629012
 
0.1%
627646
0.2%

_embedded.show.url
Categorical

HIGH CARDINALITY

Distinct633
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
https://www.tvmaze.com/shows/15250/the-young-turks
 
38
https://www.tvmaze.com/shows/55355/90-day-fiance-extras
 
32
https://www.tvmaze.com/shows/30606/scishow
 
31
https://www.tvmaze.com/shows/52743/the-penalty-zone
 
30
https://www.tvmaze.com/shows/52104/twisted-fate-of-love
 
29
Other values (628)
2922 

Length

Max length85
Median length70
Mean length50.89649578
Min length38

Characters and Unicode

Total characters156863
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)4.0%

Sample

1st rowhttps://www.tvmaze.com/shows/41648/sim-for-you
2nd rowhttps://www.tvmaze.com/shows/52198/kotiki
3rd rowhttps://www.tvmaze.com/shows/52933/lab-s-antonom-belaevym
4th rowhttps://www.tvmaze.com/shows/51336/core-sense
5th rowhttps://www.tvmaze.com/shows/54033/wu-shen-zhu-zai

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/15250/the-young-turks38
 
1.2%
https://www.tvmaze.com/shows/55355/90-day-fiance-extras32
 
1.0%
https://www.tvmaze.com/shows/30606/scishow31
 
1.0%
https://www.tvmaze.com/shows/52743/the-penalty-zone30
 
1.0%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love29
 
0.9%
https://www.tvmaze.com/shows/52421/you-complete-me28
 
0.9%
https://www.tvmaze.com/shows/52806/ultimate-note26
 
0.8%
https://www.tvmaze.com/shows/47912/the-wolf26
 
0.8%
https://www.tvmaze.com/shows/52655/the-case-solver24
 
0.8%
https://www.tvmaze.com/shows/52524/forever-love24
 
0.8%
Other values (623)2794
90.7%

Length

2022-09-17T21:55:03.948261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/15250/the-young-turks38
 
1.2%
https://www.tvmaze.com/shows/55355/90-day-fiance-extras32
 
1.0%
https://www.tvmaze.com/shows/30606/scishow31
 
1.0%
https://www.tvmaze.com/shows/52743/the-penalty-zone30
 
1.0%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love29
 
0.9%
https://www.tvmaze.com/shows/52421/you-complete-me28
 
0.9%
https://www.tvmaze.com/shows/52806/ultimate-note26
 
0.8%
https://www.tvmaze.com/shows/47912/the-wolf26
 
0.8%
https://www.tvmaze.com/shows/52655/the-case-solver24
 
0.8%
https://www.tvmaze.com/shows/52524/forever-love24
 
0.8%
Other values (623)2794
90.7%

Most occurring characters

ValueCountFrequency (%)
/15410
 
9.8%
w13063
 
8.3%
t12514
 
8.0%
s12112
 
7.7%
o9324
 
5.9%
e8458
 
5.4%
h7759
 
4.9%
m7438
 
4.7%
a6555
 
4.2%
.6164
 
3.9%
Other values (30)58066
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter110878
70.7%
Other Punctuation24656
 
15.7%
Decimal Number15699
 
10.0%
Dash Punctuation5630
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w13063
11.8%
t12514
11.3%
s12112
10.9%
o9324
 
8.4%
e8458
 
7.6%
h7759
 
7.0%
m7438
 
6.7%
a6555
 
5.9%
c4213
 
3.8%
p3819
 
3.4%
Other values (16)25623
23.1%
Decimal Number
ValueCountFrequency (%)
52967
18.9%
21949
12.4%
41868
11.9%
11576
10.0%
01371
8.7%
61336
8.5%
31320
8.4%
91170
 
7.5%
81075
 
6.8%
71067
 
6.8%
Other Punctuation
ValueCountFrequency (%)
/15410
62.5%
.6164
 
25.0%
:3082
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-5630
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin110878
70.7%
Common45985
29.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
w13063
11.8%
t12514
11.3%
s12112
10.9%
o9324
 
8.4%
e8458
 
7.6%
h7759
 
7.0%
m7438
 
6.7%
a6555
 
5.9%
c4213
 
3.8%
p3819
 
3.4%
Other values (16)25623
23.1%
Common
ValueCountFrequency (%)
/15410
33.5%
.6164
 
13.4%
-5630
 
12.2%
:3082
 
6.7%
52967
 
6.5%
21949
 
4.2%
41868
 
4.1%
11576
 
3.4%
01371
 
3.0%
61336
 
2.9%
Other values (4)4632
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII156863
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/15410
 
9.8%
w13063
 
8.3%
t12514
 
8.0%
s12112
 
7.7%
o9324
 
5.9%
e8458
 
5.4%
h7759
 
4.9%
m7438
 
4.7%
a6555
 
4.2%
.6164
 
3.9%
Other values (30)58066
37.0%

_embedded.show.name
Categorical

HIGH CARDINALITY

Distinct631
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
The Young Turks
 
38
90 Day Fiancé: Extras
 
32
SciShow
 
31
The Penalty Zone
 
30
Twisted Fate of Love
 
29
Other values (626)
2922 

Length

Max length51
Median length37
Mean length16.17780662
Min length3

Characters and Unicode

Total characters49860
Distinct characters173
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)4.0%

Sample

1st rowSim for You
2nd rowКотики
3rd rowLAB с Антоном Беляевым
4th rowCore Sense
5th rowWu Shen Zhu Zai

Common Values

ValueCountFrequency (%)
The Young Turks38
 
1.2%
90 Day Fiancé: Extras32
 
1.0%
SciShow31
 
1.0%
The Penalty Zone30
 
1.0%
Twisted Fate of Love29
 
0.9%
You Complete Me28
 
0.9%
Ultimate Note26
 
0.8%
The Wolf26
 
0.8%
The Case Solver24
 
0.8%
Youths in the Breeze24
 
0.8%
Other values (621)2794
90.7%

Length

2022-09-17T21:55:04.203663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the464
 
5.3%
of190
 
2.2%
love124
 
1.4%
you79
 
0.9%
in68
 
0.8%
a61
 
0.7%
to51
 
0.6%
my49
 
0.6%
me44
 
0.5%
with44
 
0.5%
Other values (1297)7524
86.5%

Most occurring characters

ValueCountFrequency (%)
5616
 
11.3%
e4797
 
9.6%
a2784
 
5.6%
o2669
 
5.4%
i2457
 
4.9%
n2433
 
4.9%
r2403
 
4.8%
t2207
 
4.4%
s1968
 
3.9%
l1528
 
3.1%
Other values (163)20998
42.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter35188
70.6%
Uppercase Letter7897
 
15.8%
Space Separator5616
 
11.3%
Other Punctuation706
 
1.4%
Decimal Number376
 
0.8%
Dash Punctuation57
 
0.1%
Close Punctuation8
 
< 0.1%
Currency Symbol8
 
< 0.1%
Open Punctuation4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4797
13.6%
a2784
 
7.9%
o2669
 
7.6%
i2457
 
7.0%
n2433
 
6.9%
r2403
 
6.8%
t2207
 
6.3%
s1968
 
5.6%
l1528
 
4.3%
h1319
 
3.7%
Other values (75)10623
30.2%
Uppercase Letter
ValueCountFrequency (%)
T904
 
11.4%
S677
 
8.6%
B522
 
6.6%
M477
 
6.0%
L437
 
5.5%
C416
 
5.3%
W412
 
5.2%
F386
 
4.9%
D364
 
4.6%
A353
 
4.5%
Other values (49)2949
37.3%
Other Punctuation
ValueCountFrequency (%)
:257
36.4%
'150
21.2%
.102
 
14.4%
!69
 
9.8%
,50
 
7.1%
?34
 
4.8%
&22
 
3.1%
%8
 
1.1%
#6
 
0.8%
@4
 
0.6%
Other values (2)4
 
0.6%
Decimal Number
ValueCountFrequency (%)
0133
35.4%
293
24.7%
342
 
11.2%
932
 
8.5%
132
 
8.5%
715
 
4.0%
514
 
3.7%
69
 
2.4%
84
 
1.1%
42
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
-54
94.7%
3
 
5.3%
Currency Symbol
ValueCountFrequency (%)
$4
50.0%
4
50.0%
Space Separator
ValueCountFrequency (%)
5616
100.0%
Close Punctuation
ValueCountFrequency (%)
)8
100.0%
Open Punctuation
ValueCountFrequency (%)
(4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin40314
80.9%
Common6775
 
13.6%
Cyrillic2603
 
5.2%
Greek168
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4797
 
11.9%
a2784
 
6.9%
o2669
 
6.6%
i2457
 
6.1%
n2433
 
6.0%
r2403
 
6.0%
t2207
 
5.5%
s1968
 
4.9%
l1528
 
3.8%
h1319
 
3.3%
Other values (57)15749
39.1%
Cyrillic
ValueCountFrequency (%)
о237
 
9.1%
е197
 
7.6%
а190
 
7.3%
и176
 
6.8%
к148
 
5.7%
н145
 
5.6%
т138
 
5.3%
р129
 
5.0%
с108
 
4.1%
д70
 
2.7%
Other values (50)1065
40.9%
Common
ValueCountFrequency (%)
5616
82.9%
:257
 
3.8%
'150
 
2.2%
0133
 
2.0%
.102
 
1.5%
293
 
1.4%
!69
 
1.0%
-54
 
0.8%
,50
 
0.7%
342
 
0.6%
Other values (19)209
 
3.1%
Greek
ValueCountFrequency (%)
ς24
14.3%
ε16
 
9.5%
έ16
 
9.5%
ρ8
 
4.8%
Χ8
 
4.8%
Έ8
 
4.8%
γ8
 
4.8%
Ε8
 
4.8%
τ8
 
4.8%
α8
 
4.8%
Other values (7)56
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII46854
94.0%
Cyrillic2603
 
5.2%
None396
 
0.8%
Currency Symbols4
 
< 0.1%
Punctuation3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5616
 
12.0%
e4797
 
10.2%
a2784
 
5.9%
o2669
 
5.7%
i2457
 
5.2%
n2433
 
5.2%
r2403
 
5.1%
t2207
 
4.7%
s1968
 
4.2%
l1528
 
3.3%
Other values (69)17992
38.4%
Cyrillic
ValueCountFrequency (%)
о237
 
9.1%
е197
 
7.6%
а190
 
7.3%
и176
 
6.8%
к148
 
5.7%
н145
 
5.6%
т138
 
5.3%
р129
 
5.0%
с108
 
4.1%
д70
 
2.7%
Other values (50)1065
40.9%
None
ValueCountFrequency (%)
ø63
15.9%
é52
 
13.1%
ä26
 
6.6%
å25
 
6.3%
ς24
 
6.1%
ε16
 
4.0%
έ16
 
4.0%
ı11
 
2.8%
á10
 
2.5%
Ç8
 
2.0%
Other values (22)145
36.6%
Currency Symbols
ValueCountFrequency (%)
4
100.0%
Punctuation
ValueCountFrequency (%)
3
100.0%

_embedded.show.type
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
Scripted
1593 
Animation
385 
Documentary
328 
Reality
264 
Talk Show
242 
Other values (6)
270 

Length

Max length11
Median length8
Mean length8.308241402
Min length4

Characters and Unicode

Total characters25606
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReality
2nd rowScripted
3rd rowDocumentary
4th rowAnimation
5th rowAnimation

Common Values

ValueCountFrequency (%)
Scripted1593
51.7%
Animation385
 
12.5%
Documentary328
 
10.6%
Reality264
 
8.6%
Talk Show242
 
7.9%
Variety79
 
2.6%
Sports79
 
2.6%
News56
 
1.8%
Game Show48
 
1.6%
Award Show6
 
0.2%

Length

2022-09-17T21:55:04.432664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
scripted1593
47.1%
animation385
 
11.4%
documentary328
 
9.7%
show298
 
8.8%
reality264
 
7.8%
talk242
 
7.2%
variety79
 
2.3%
sports79
 
2.3%
news56
 
1.7%
game48
 
1.4%
Other values (2)8
 
0.2%

Most occurring characters

ValueCountFrequency (%)
t2728
10.7%
i2706
10.6%
e2370
 
9.3%
r2085
 
8.1%
S1970
 
7.7%
c1921
 
7.5%
p1672
 
6.5%
d1599
 
6.2%
a1354
 
5.3%
n1100
 
4.3%
Other values (18)6101
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21928
85.6%
Uppercase Letter3380
 
13.2%
Space Separator298
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t2728
12.4%
i2706
12.3%
e2370
10.8%
r2085
9.5%
c1921
8.8%
p1672
7.6%
d1599
7.3%
a1354
6.2%
n1100
 
5.0%
o1090
 
5.0%
Other values (8)3303
15.1%
Uppercase Letter
ValueCountFrequency (%)
S1970
58.3%
A391
 
11.6%
D328
 
9.7%
R264
 
7.8%
T242
 
7.2%
V79
 
2.3%
N56
 
1.7%
G48
 
1.4%
P2
 
0.1%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25308
98.8%
Common298
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t2728
10.8%
i2706
10.7%
e2370
9.4%
r2085
 
8.2%
S1970
 
7.8%
c1921
 
7.6%
p1672
 
6.6%
d1599
 
6.3%
a1354
 
5.4%
n1100
 
4.3%
Other values (17)5803
22.9%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t2728
10.7%
i2706
10.6%
e2370
 
9.3%
r2085
 
8.1%
S1970
 
7.7%
c1921
 
7.5%
p1672
 
6.5%
d1599
 
6.2%
a1354
 
5.3%
n1100
 
4.3%
Other values (18)6101
23.8%

_embedded.show.language
Categorical

HIGH CORRELATION
MISSING

Distinct37
Distinct (%)1.2%
Missing34
Missing (%)1.1%
Memory size24.2 KiB
English
983 
Chinese
686 
Russian
246 
Norwegian
223 
Korean
181 
Other values (32)
729 

Length

Max length10
Median length7
Mean length6.982283465
Min length4

Characters and Unicode

Total characters21282
Distinct characters43
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKorean
2nd rowRussian
3rd rowRussian
4th rowChinese
5th rowChinese

Common Values

ValueCountFrequency (%)
English983
31.9%
Chinese686
22.3%
Russian246
 
8.0%
Norwegian223
 
7.2%
Korean181
 
5.9%
Arabic66
 
2.1%
Japanese64
 
2.1%
Hindi64
 
2.1%
Spanish60
 
1.9%
Thai51
 
1.7%
Other values (27)424
13.8%

Length

2022-09-17T21:55:04.632340image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english983
32.3%
chinese686
22.5%
russian246
 
8.1%
norwegian223
 
7.3%
korean181
 
5.9%
arabic66
 
2.2%
japanese64
 
2.1%
hindi64
 
2.1%
spanish60
 
2.0%
thai51
 
1.7%
Other values (27)424
13.9%

Most occurring characters

ValueCountFrequency (%)
n2725
12.8%
i2673
12.6%
s2446
11.5%
e2165
10.2%
h1967
9.2%
g1357
 
6.4%
a1252
 
5.9%
l1091
 
5.1%
E983
 
4.6%
C686
 
3.2%
Other values (33)3937
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter18234
85.7%
Uppercase Letter3048
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n2725
14.9%
i2673
14.7%
s2446
13.4%
e2165
11.9%
h1967
10.8%
g1357
7.4%
a1252
6.9%
l1091
6.0%
r648
 
3.6%
o508
 
2.8%
Other values (13)1402
7.7%
Uppercase Letter
ValueCountFrequency (%)
E983
32.3%
C686
22.5%
R250
 
8.2%
N223
 
7.3%
K185
 
6.1%
T139
 
4.6%
S91
 
3.0%
H73
 
2.4%
P69
 
2.3%
A66
 
2.2%
Other values (10)283
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Latin21282
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n2725
12.8%
i2673
12.6%
s2446
11.5%
e2165
10.2%
h1967
9.2%
g1357
 
6.4%
a1252
 
5.9%
l1091
 
5.1%
E983
 
4.6%
C686
 
3.2%
Other values (33)3937
18.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII21282
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n2725
12.8%
i2673
12.6%
s2446
11.5%
e2165
10.2%
h1967
9.2%
g1357
 
6.4%
a1252
 
5.9%
l1091
 
5.1%
E983
 
4.6%
C686
 
3.2%
Other values (33)3937
18.5%

_embedded.show.genres
Categorical

HIGH CARDINALITY

Distinct160
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
[]
779 
['Comedy']
275 
['Drama', 'Romance']
268 
['Drama']
 
96
['Drama', 'Comedy', 'Romance']
 
84
Other values (155)
1580 

Length

Max length51
Median length43
Mean length17.28909799
Min length2

Characters and Unicode

Total characters53285
Distinct characters38
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.5%

Sample

1st row[]
2nd row['Comedy']
3rd row['Music']
4th row['Action', 'Anime', 'Science-Fiction']
5th row['Action', 'Adventure', 'Anime', 'Fantasy']

Common Values

ValueCountFrequency (%)
[]779
25.3%
['Comedy']275
 
8.9%
['Drama', 'Romance']268
 
8.7%
['Drama']96
 
3.1%
['Drama', 'Comedy', 'Romance']84
 
2.7%
['Drama', 'Romance', 'History']71
 
2.3%
['Drama', 'Comedy']63
 
2.0%
['Crime']61
 
2.0%
['Sports']51
 
1.7%
['Drama', 'Thriller', 'Mystery']50
 
1.6%
Other values (150)1284
41.7%

Length

2022-09-17T21:55:04.857305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drama1100
19.1%
779
13.5%
comedy751
13.1%
romance579
10.1%
crime291
 
5.1%
mystery235
 
4.1%
fantasy228
 
4.0%
action226
 
3.9%
thriller223
 
3.9%
adventure187
 
3.3%
Other values (17)1152
20.0%

Most occurring characters

ValueCountFrequency (%)
'9944
18.7%
a3593
 
6.7%
e3102
 
5.8%
r3099
 
5.8%
[3082
 
5.8%
]3082
 
5.8%
m2967
 
5.6%
2669
 
5.0%
,2669
 
5.0%
o2103
 
3.9%
Other values (28)16975
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter26673
50.1%
Other Punctuation12613
23.7%
Uppercase Letter5073
 
9.5%
Open Punctuation3082
 
5.8%
Close Punctuation3082
 
5.8%
Space Separator2669
 
5.0%
Dash Punctuation93
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a3593
13.5%
e3102
11.6%
r3099
11.6%
m2967
11.1%
o2103
7.9%
n1804
6.8%
i1713
6.4%
y1700
6.4%
t1288
 
4.8%
c1207
 
4.5%
Other values (8)4097
15.4%
Uppercase Letter
ValueCountFrequency (%)
C1223
24.1%
D1104
21.8%
R579
11.4%
A563
11.1%
F480
 
9.5%
M358
 
7.1%
T253
 
5.0%
S235
 
4.6%
H215
 
4.2%
W30
 
0.6%
Other values (4)33
 
0.7%
Other Punctuation
ValueCountFrequency (%)
'9944
78.8%
,2669
 
21.2%
Open Punctuation
ValueCountFrequency (%)
[3082
100.0%
Close Punctuation
ValueCountFrequency (%)
]3082
100.0%
Space Separator
ValueCountFrequency (%)
2669
100.0%
Dash Punctuation
ValueCountFrequency (%)
-93
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin31746
59.6%
Common21539
40.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a3593
 
11.3%
e3102
 
9.8%
r3099
 
9.8%
m2967
 
9.3%
o2103
 
6.6%
n1804
 
5.7%
i1713
 
5.4%
y1700
 
5.4%
t1288
 
4.1%
C1223
 
3.9%
Other values (22)9154
28.8%
Common
ValueCountFrequency (%)
'9944
46.2%
[3082
 
14.3%
]3082
 
14.3%
2669
 
12.4%
,2669
 
12.4%
-93
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII53285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'9944
18.7%
a3593
 
6.7%
e3102
 
5.8%
r3099
 
5.8%
[3082
 
5.8%
]3082
 
5.8%
m2967
 
5.6%
2669
 
5.0%
,2669
 
5.0%
o2103
 
3.9%
Other values (28)16975
31.9%

_embedded.show.status
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
Ended
1383 
Running
1319 
To Be Determined
380 

Length

Max length16
Median length7
Mean length7.21219987
Min length5

Characters and Unicode

Total characters22228
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRunning
2nd rowEnded
3rd rowTo Be Determined
4th rowRunning
5th rowRunning

Common Values

ValueCountFrequency (%)
Ended1383
44.9%
Running1319
42.8%
To Be Determined380
 
12.3%

Length

2022-09-17T21:55:05.056992image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-17T21:55:05.229895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
ended1383
36.0%
running1319
34.3%
to380
 
9.9%
be380
 
9.9%
determined380
 
9.9%

Most occurring characters

ValueCountFrequency (%)
n5720
25.7%
d3146
14.2%
e2903
13.1%
i1699
 
7.6%
E1383
 
6.2%
R1319
 
5.9%
u1319
 
5.9%
g1319
 
5.9%
760
 
3.4%
T380
 
1.7%
Other values (6)2280
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17626
79.3%
Uppercase Letter3842
 
17.3%
Space Separator760
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n5720
32.5%
d3146
17.8%
e2903
16.5%
i1699
 
9.6%
u1319
 
7.5%
g1319
 
7.5%
o380
 
2.2%
t380
 
2.2%
r380
 
2.2%
m380
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
E1383
36.0%
R1319
34.3%
T380
 
9.9%
B380
 
9.9%
D380
 
9.9%
Space Separator
ValueCountFrequency (%)
760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21468
96.6%
Common760
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n5720
26.6%
d3146
14.7%
e2903
13.5%
i1699
 
7.9%
E1383
 
6.4%
R1319
 
6.1%
u1319
 
6.1%
g1319
 
6.1%
T380
 
1.8%
o380
 
1.8%
Other values (5)1900
 
8.9%
Common
ValueCountFrequency (%)
760
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII22228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n5720
25.7%
d3146
14.2%
e2903
13.1%
i1699
 
7.6%
E1383
 
6.2%
R1319
 
5.9%
u1319
 
5.9%
g1319
 
5.9%
760
 
3.4%
T380
 
1.7%
Other values (6)2280
 
10.3%

_embedded.show.runtime
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct56
Distinct (%)2.7%
Missing1017
Missing (%)33.0%
Infinite0
Infinite (%)0.0%
Mean38.52736077
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T21:55:05.419368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q120
median34
Q345
95-th percentile120
Maximum300
Range299
Interquartile range (IQR)25

Descriptive statistics

Standard deviation30.73545147
Coefficient of variation (CV)0.7977564735
Kurtosis15.48581094
Mean38.52736077
Median Absolute Deviation (MAD)14
Skewness2.987837392
Sum79559
Variance944.6679768
MonotonicityNot monotonic
2022-09-17T21:55:05.638805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45490
15.9%
30192
 
6.2%
20165
 
5.4%
60154
 
5.0%
1590
 
2.9%
12088
 
2.9%
2582
 
2.7%
5062
 
2.0%
1062
 
2.0%
4062
 
2.0%
Other values (46)618
20.1%
(Missing)1017
33.0%
ValueCountFrequency (%)
14
 
0.1%
210
 
0.3%
33
 
0.1%
418
 
0.6%
545
1.5%
67
 
0.2%
739
1.3%
838
1.2%
97
 
0.2%
1062
2.0%
ValueCountFrequency (%)
3004
 
0.1%
2403
 
0.1%
18014
 
0.5%
1304
 
0.1%
12088
2.9%
9023
 
0.7%
661
 
< 0.1%
625
 
0.2%
60154
5.0%
582
 
0.1%
Distinct407
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
Minimum1969-11-10 00:00:00
Maximum2020-12-31 00:00:00
2022-09-17T21:55:05.863619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:55:06.107563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct112
Distinct (%)8.1%
Missing1699
Missing (%)55.1%
Memory size24.2 KiB
Minimum2020-10-25 00:00:00
Maximum2023-01-12 00:00:00
2022-09-17T21:55:06.363820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:55:06.580382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

_embedded.show.officialSite
Categorical

HIGH CARDINALITY
MISSING

Distinct570
Distinct (%)21.3%
Missing408
Missing (%)13.2%
Memory size24.2 KiB
https://www.tytnetwork.com
 
38
https://www.discoveryplus.co.uk/show/90-day-extras
 
32
https://www.youtube.com/user/scishow/
 
31
https://www.iqiyi.com/a_19rrhllpip.html
 
30
https://v.qq.com/x/search/?q=+%E4%BB%8A%E5%A4%95%E4%BD%95%E5%A4%95&stag=0&smartbox_ab=
 
29
Other values (565)
2514 

Length

Max length250
Median length89
Mean length51.23635004
Min length15

Characters and Unicode

Total characters137006
Distinct characters77
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111 ?
Unique (%)4.2%

Sample

1st rowhttps://www.vlive.tv/video/121637
2nd rowhttp://epic-media.ru/project/kotiki
3rd rowhttps://premier.one/show/lab-laboratoriya-muzyki-antona-belyaeva
4th rowhttps://www.bilibili.com/bangumi/media/md28223064
5th rowhttps://v.qq.com/detail/m/7q544xyrava3vxf.html

Common Values

ValueCountFrequency (%)
https://www.tytnetwork.com38
 
1.2%
https://www.discoveryplus.co.uk/show/90-day-extras32
 
1.0%
https://www.youtube.com/user/scishow/31
 
1.0%
https://www.iqiyi.com/a_19rrhllpip.html30
 
1.0%
https://v.qq.com/x/search/?q=+%E4%BB%8A%E5%A4%95%E4%BD%95%E5%A4%95&stag=0&smartbox_ab=29
 
0.9%
https://www.iqiyi.com/a_nvzsmw0tgx.html26
 
0.8%
https://www.iqiyi.com/lib/m_213579814.html26
 
0.8%
https://tv.nrk.no/serie/stjernestoev24
 
0.8%
https://v.youku.com/v_show/id_XNDk4OTUxMzg1Mg==.html?spm=a2hbt.13141534.0.13141534&s=6eefbfbd4befbfbd32ef24
 
0.8%
https://www.iqiyi.com/a_c4m3iuc94t.html24
 
0.8%
Other values (560)2390
77.5%
(Missing)408
 
13.2%

Length

2022-09-17T21:55:06.834283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tytnetwork.com38
 
1.4%
https://www.discoveryplus.co.uk/show/90-day-extras32
 
1.2%
https://www.youtube.com/user/scishow31
 
1.2%
https://www.iqiyi.com/a_19rrhllpip.html30
 
1.1%
https://v.qq.com/x/search/?q=+%e4%bb%8a%e5%a4%95%e4%bd%95%e5%a4%95&stag=0&smartbox_ab29
 
1.1%
https://www.iqiyi.com/a_nvzsmw0tgx.html26
 
1.0%
https://www.iqiyi.com/lib/m_213579814.html26
 
1.0%
https://tv.nrk.no/serie/stjernestoev24
 
0.9%
https://v.youku.com/v_show/id_xndk4otuxmzg1mg==.html?spm=a2hbt.13141534.0.13141534&s=6eefbfbd4befbfbd32ef24
 
0.9%
https://www.iqiyi.com/a_c4m3iuc94t.html24
 
0.9%
Other values (559)2390
89.4%

Most occurring characters

ValueCountFrequency (%)
/11240
 
8.2%
t10538
 
7.7%
s6976
 
5.1%
e6915
 
5.0%
w6070
 
4.4%
o5897
 
4.3%
.5648
 
4.1%
h5123
 
3.7%
i4793
 
3.5%
p4441
 
3.2%
Other values (67)69365
50.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter90251
65.9%
Other Punctuation21749
 
15.9%
Decimal Number13874
 
10.1%
Uppercase Letter7405
 
5.4%
Dash Punctuation2421
 
1.8%
Math Symbol735
 
0.5%
Connector Punctuation571
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t10538
 
11.7%
s6976
 
7.7%
e6915
 
7.7%
w6070
 
6.7%
o5897
 
6.5%
h5123
 
5.7%
i4793
 
5.3%
p4441
 
4.9%
a4332
 
4.8%
m4082
 
4.5%
Other values (16)31084
34.4%
Uppercase Letter
ValueCountFrequency (%)
E602
 
8.1%
A574
 
7.8%
B513
 
6.9%
P460
 
6.2%
C432
 
5.8%
D365
 
4.9%
L364
 
4.9%
N309
 
4.2%
T279
 
3.8%
M268
 
3.6%
Other values (16)3239
43.7%
Other Punctuation
ValueCountFrequency (%)
/11240
51.7%
.5648
26.0%
:2930
 
13.5%
%1288
 
5.9%
?382
 
1.8%
&199
 
0.9%
!21
 
0.1%
#21
 
0.1%
'10
 
< 0.1%
,10
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
02108
15.2%
11781
12.8%
81432
10.3%
21392
10.0%
41384
10.0%
91352
9.7%
31243
9.0%
51166
8.4%
61058
7.6%
7958
6.9%
Math Symbol
ValueCountFrequency (%)
=676
92.0%
+43
 
5.9%
~16
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-2421
100.0%
Connector Punctuation
ValueCountFrequency (%)
_571
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin97656
71.3%
Common39350
28.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t10538
 
10.8%
s6976
 
7.1%
e6915
 
7.1%
w6070
 
6.2%
o5897
 
6.0%
h5123
 
5.2%
i4793
 
4.9%
p4441
 
4.5%
a4332
 
4.4%
m4082
 
4.2%
Other values (42)38489
39.4%
Common
ValueCountFrequency (%)
/11240
28.6%
.5648
14.4%
:2930
 
7.4%
-2421
 
6.2%
02108
 
5.4%
11781
 
4.5%
81432
 
3.6%
21392
 
3.5%
41384
 
3.5%
91352
 
3.4%
Other values (15)7662
19.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII137006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/11240
 
8.2%
t10538
 
7.7%
s6976
 
5.1%
e6915
 
5.0%
w6070
 
4.4%
o5897
 
4.3%
.5648
 
4.1%
h5123
 
3.7%
i4793
 
3.5%
p4441
 
3.2%
Other values (67)69365
50.6%

_embedded.show.schedule.time
Categorical

HIGH CORRELATION
MISSING

Distinct44
Distinct (%)5.1%
Missing2213
Missing (%)71.8%
Memory size24.2 KiB
20:00
284 
10:00
75 
12:00
71 
06:00
61 
21:00
56 
Other values (39)
322 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4345
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.7%

Sample

1st row10:00
2nd row23:45
3rd row10:00
4th row10:00
5th row22:00

Common Values

ValueCountFrequency (%)
20:00284
 
9.2%
10:0075
 
2.4%
12:0071
 
2.3%
06:0061
 
2.0%
21:0056
 
1.8%
19:0043
 
1.4%
18:0039
 
1.3%
00:0028
 
0.9%
22:0026
 
0.8%
17:0022
 
0.7%
Other values (34)164
 
5.3%
(Missing)2213
71.8%

Length

2022-09-17T21:55:07.023763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20:00284
32.7%
10:0075
 
8.6%
12:0071
 
8.2%
06:0061
 
7.0%
21:0056
 
6.4%
19:0043
 
4.9%
18:0039
 
4.5%
00:0028
 
3.2%
22:0026
 
3.0%
17:0022
 
2.5%
Other values (34)164
18.9%

Most occurring characters

ValueCountFrequency (%)
02138
49.2%
:869
20.0%
2556
 
12.8%
1414
 
9.5%
671
 
1.6%
365
 
1.5%
861
 
1.4%
560
 
1.4%
954
 
1.2%
735
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3476
80.0%
Other Punctuation869
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02138
61.5%
2556
 
16.0%
1414
 
11.9%
671
 
2.0%
365
 
1.9%
861
 
1.8%
560
 
1.7%
954
 
1.6%
735
 
1.0%
422
 
0.6%
Other Punctuation
ValueCountFrequency (%)
:869
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02138
49.2%
:869
20.0%
2556
 
12.8%
1414
 
9.5%
671
 
1.6%
365
 
1.5%
861
 
1.4%
560
 
1.4%
954
 
1.2%
735
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02138
49.2%
:869
20.0%
2556
 
12.8%
1414
 
9.5%
671
 
1.6%
365
 
1.5%
861
 
1.4%
560
 
1.4%
954
 
1.2%
735
 
0.8%

_embedded.show.schedule.days
Categorical

HIGH CORRELATION

Distinct41
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
[]
879 
['Friday']
297 
['Thursday']
262 
['Monday']
173 
['Tuesday']
146 
Other values (36)
1325 

Length

Max length78
Median length57
Mean length17.50908501
Min length2

Characters and Unicode

Total characters53963
Distinct characters22
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row['Monday', 'Wednesday', 'Friday']
2nd row['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
3rd row['Saturday']
4th row['Tuesday']
5th row['Tuesday', 'Sunday']

Common Values

ValueCountFrequency (%)
[]879
28.5%
['Friday']297
 
9.6%
['Thursday']262
 
8.5%
['Monday']173
 
5.6%
['Tuesday']146
 
4.7%
['Wednesday']142
 
4.6%
['Saturday']139
 
4.5%
['Sunday']120
 
3.9%
['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']110
 
3.6%
['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']92
 
3.0%
Other values (31)722
23.4%

Length

2022-09-17T21:55:07.216889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
thursday899
16.3%
879
15.9%
wednesday804
14.5%
friday753
13.6%
monday723
13.1%
tuesday703
12.7%
saturday401
7.3%
sunday366
6.6%

Most occurring characters

ValueCountFrequency (%)
'9298
17.2%
d5453
 
10.1%
a5050
 
9.4%
y4649
 
8.6%
[3082
 
5.7%
]3082
 
5.7%
,2446
 
4.5%
2446
 
4.5%
s2406
 
4.5%
u2369
 
4.4%
Other values (12)13682
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28960
53.7%
Other Punctuation11744
21.8%
Uppercase Letter4649
 
8.6%
Open Punctuation3082
 
5.7%
Close Punctuation3082
 
5.7%
Space Separator2446
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d5453
18.8%
a5050
17.4%
y4649
16.1%
s2406
8.3%
u2369
8.2%
e2311
8.0%
r2053
 
7.1%
n1893
 
6.5%
h899
 
3.1%
i753
 
2.6%
Other values (2)1124
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
T1602
34.5%
W804
17.3%
S767
16.5%
F753
16.2%
M723
15.6%
Other Punctuation
ValueCountFrequency (%)
'9298
79.2%
,2446
 
20.8%
Open Punctuation
ValueCountFrequency (%)
[3082
100.0%
Close Punctuation
ValueCountFrequency (%)
]3082
100.0%
Space Separator
ValueCountFrequency (%)
2446
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin33609
62.3%
Common20354
37.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
d5453
16.2%
a5050
15.0%
y4649
13.8%
s2406
7.2%
u2369
7.0%
e2311
6.9%
r2053
 
6.1%
n1893
 
5.6%
T1602
 
4.8%
h899
 
2.7%
Other values (7)4924
14.7%
Common
ValueCountFrequency (%)
'9298
45.7%
[3082
 
15.1%
]3082
 
15.1%
,2446
 
12.0%
2446
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII53963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'9298
17.2%
d5453
 
10.1%
a5050
 
9.4%
y4649
 
8.6%
[3082
 
5.7%
]3082
 
5.7%
,2446
 
4.5%
2446
 
4.5%
s2406
 
4.5%
u2369
 
4.4%
Other values (12)13682
25.4%

_embedded.show.rating.average
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct33
Distinct (%)7.8%
Missing2661
Missing (%)86.3%
Infinite0
Infinite (%)0.0%
Mean6.864608076
Minimum3.6
Maximum8.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T21:55:07.413971image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.6
5-th percentile5
Q16.6
median7.1
Q37.5
95-th percentile8.1
Maximum8.8
Range5.2
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.93839105
Coefficient of variation (CV)0.1366998727
Kurtosis0.2982410094
Mean6.864608076
Median Absolute Deviation (MAD)0.4
Skewness-0.7695587937
Sum2890
Variance0.8805777627
MonotonicityNot monotonic
2022-09-17T21:55:07.617614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
7.244
 
1.4%
7.733
 
1.1%
7.532
 
1.0%
6.829
 
0.9%
6.728
 
0.9%
7.422
 
0.7%
721
 
0.7%
521
 
0.7%
6.620
 
0.6%
7.315
 
0.5%
Other values (23)156
 
5.1%
(Missing)2661
86.3%
ValueCountFrequency (%)
3.62
 
0.1%
41
 
< 0.1%
4.31
 
< 0.1%
4.82
 
0.1%
521
0.7%
5.28
 
0.3%
5.314
0.5%
5.412
0.4%
5.64
 
0.1%
5.88
 
0.3%
ValueCountFrequency (%)
8.85
 
0.2%
8.63
 
0.1%
8.212
 
0.4%
8.113
 
0.4%
85
 
0.2%
7.810
 
0.3%
7.733
1.1%
7.64
 
0.1%
7.532
1.0%
7.422
0.7%

_embedded.show.weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct98
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.13108371
Minimum0
Maximum100
Zeros15
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T21:55:07.837758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q117
median29
Q348
95-th percentile91
Maximum100
Range100
Interquartile range (IQR)31

Descriptive statistics

Standard deviation26.1981504
Coefficient of variation (CV)0.7250862057
Kurtosis-0.2769065556
Mean36.13108371
Median Absolute Deviation (MAD)14
Skewness0.8458025265
Sum111356
Variance686.3430841
MonotonicityNot monotonic
2022-09-17T21:55:08.062971image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19113
 
3.7%
29113
 
3.7%
18112
 
3.6%
16100
 
3.2%
2499
 
3.2%
1587
 
2.8%
786
 
2.8%
479
 
2.6%
1770
 
2.3%
3964
 
2.1%
Other values (88)2159
70.1%
ValueCountFrequency (%)
015
 
0.5%
146
1.5%
238
1.2%
345
1.5%
479
2.6%
53
 
0.1%
611
 
0.4%
786
2.8%
859
1.9%
946
1.5%
ValueCountFrequency (%)
1009
 
0.3%
9911
 
0.4%
988
 
0.3%
9725
0.8%
9633
1.1%
956
 
0.2%
942
 
0.1%
9346
1.5%
9214
 
0.5%
9121
0.7%

_embedded.show.network
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3082
Missing (%)100.0%
Memory size24.2 KiB

_embedded.show.averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct90
Distinct (%)3.1%
Missing169
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean36.8115345
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T21:55:08.286233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q118
median30
Q345
95-th percentile91
Maximum300
Range299
Interquartile range (IQR)27

Descriptive statistics

Standard deviation28.10140055
Coefficient of variation (CV)0.7633857413
Kurtosis15.38266314
Mean36.8115345
Median Absolute Deviation (MAD)15
Skewness2.775943319
Sum107232
Variance789.6887131
MonotonicityNot monotonic
2022-09-17T21:55:08.510381image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45465
 
15.1%
30175
 
5.7%
60129
 
4.2%
20124
 
4.0%
25116
 
3.8%
12107
 
3.5%
5086
 
2.8%
1584
 
2.7%
12070
 
2.3%
566
 
2.1%
Other values (80)1491
48.4%
(Missing)169
 
5.5%
ValueCountFrequency (%)
14
 
0.1%
215
 
0.5%
36
 
0.2%
420
 
0.6%
566
2.1%
617
 
0.6%
752
1.7%
846
1.5%
938
1.2%
1060
1.9%
ValueCountFrequency (%)
3004
0.1%
2123
0.1%
1942
0.1%
1931
 
< 0.1%
1883
0.1%
1814
0.1%
1802
0.1%
1351
 
< 0.1%
1304
0.1%
1292
0.1%

Interactions

2022-09-17T21:54:51.626426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:05.686888image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:31.905522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:39.014481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:41.271260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:55:00.940525image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:19.707737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:38.309246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:40.589237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:50.893788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:55:01.122794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:22.581625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:38.495875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:40.741343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:51.074580image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:55:01.291831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:23.359980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:38.651757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:40.911627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:51.256800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:55:01.466264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:27.727190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:38.827641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:41.099357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-17T21:54:51.433621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-09-17T21:55:08.705110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-17T21:55:08.942509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-17T21:55:09.186156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-17T21:55:09.426917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-17T21:55:09.659767image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-17T21:55:01.853730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-17T21:55:02.359684image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-17T21:55:02.784826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-17T21:55:03.067796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

_embedded.show.id_embedded.show.url_embedded.show.name_embedded.show.type_embedded.show.language_embedded.show.genres_embedded.show.status_embedded.show.runtime_embedded.show.premiered_embedded.show.ended_embedded.show.officialSite_embedded.show.schedule.time_embedded.show.schedule.days_embedded.show.rating.average_embedded.show.weight_embedded.show.network_embedded.show.averageRuntime
041648https://www.tvmaze.com/shows/41648/sim-for-youSim for YouRealityKorean[]Running16.02019-03-25NaThttps://www.vlive.tv/video/121637NaN['Monday', 'Wednesday', 'Friday']NaN35NaN16.0
152198https://www.tvmaze.com/shows/52198/kotikiКотикиScriptedRussian['Comedy']Ended12.02020-11-302020-12-11http://epic-media.ru/project/kotiki10:00['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']NaN16NaN12.0
252933https://www.tvmaze.com/shows/52933/lab-s-antonom-belaevymLAB с Антоном БеляевымDocumentaryRussian['Music']To Be Determined26.02019-12-17NaThttps://premier.one/show/lab-laboratoriya-muzyki-antona-belyaeva23:45['Saturday']NaN17NaN25.0
351336https://www.tvmaze.com/shows/51336/core-senseCore SenseAnimationChinese['Action', 'Anime', 'Science-Fiction']Running24.02020-10-13NaThttps://www.bilibili.com/bangumi/media/md2822306410:00['Tuesday']NaN29NaN24.0
454033https://www.tvmaze.com/shows/54033/wu-shen-zhu-zaiWu Shen Zhu ZaiAnimationChinese['Action', 'Adventure', 'Anime', 'Fantasy']Running8.02020-03-08NaThttps://v.qq.com/detail/m/7q544xyrava3vxf.html10:00['Tuesday', 'Sunday']NaN80NaN8.0
561674https://www.tvmaze.com/shows/61674/sono-koi-mousukoshi-atatamemasukaSono koi Mousukoshi AtatamemasukaScriptedJapanese['Romance']Ended15.02020-10-202020-12-22https://www.paravi.jp/static/koisuko22:00['Tuesday']NaN1NaN15.0
663967https://www.tvmaze.com/shows/63967/run-btsRun BTS!VarietyKorean[]RunningNaN2015-08-01NaTNaNNaN['Tuesday']NaN39NaNNaN
752038https://www.tvmaze.com/shows/52038/please-wait-brotherPlease Wait, BrotherScriptedChinese['Comedy']Ended37.02020-11-172020-12-08NaN12:00['Tuesday', 'Wednesday', 'Thursday']NaN19NaN37.0
852038https://www.tvmaze.com/shows/52038/please-wait-brotherPlease Wait, BrotherScriptedChinese['Comedy']Ended37.02020-11-172020-12-08NaN12:00['Tuesday', 'Wednesday', 'Thursday']NaN19NaN37.0
952373https://www.tvmaze.com/shows/52373/fearless-whispersFearless WhispersScriptedChinese['Drama', 'Romance', 'History']Ended60.02020-11-062020-12-01NaNNaN['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']NaN26NaN60.0

Last rows

_embedded.show.id_embedded.show.url_embedded.show.name_embedded.show.type_embedded.show.language_embedded.show.genres_embedded.show.status_embedded.show.runtime_embedded.show.premiered_embedded.show.ended_embedded.show.officialSite_embedded.show.schedule.time_embedded.show.schedule.days_embedded.show.rating.average_embedded.show.weight_embedded.show.network_embedded.show.averageRuntime
307259380https://www.tvmaze.com/shows/59380/forteresses-assiegees-batailles-de-legendeForteresses assiégées, batailles de légendeDocumentaryFrench[]Running51.02020-12-31NaThttps://www.zed.fr/fr/tv/distribution/catalogue/programme/forteresses-assiegees-batailles-de-legendeNaN['Thursday']NaN1NaN52.0
307317046https://www.tvmaze.com/shows/17046/notruf-hafenkanteNotruf HafenkanteScriptedGerman['Drama', 'Crime']Running45.02007-01-04NaThttps://www.zdf.de/serien/notruf-hafenkante19:25['Thursday']NaN28NaN50.0
30742504https://www.tvmaze.com/shows/2504/goede-tijden-slechte-tijdenGoede Tijden, Slechte TijdenScriptedDutch['Drama', 'Romance']Running23.01990-10-01NaThttp://gtst.nl/#!/20:00['Monday', 'Tuesday', 'Wednesday', 'Thursday']NaN80NaN25.0
307539053https://www.tvmaze.com/shows/39053/wwe-nxt-ukWWE NXT UKSportsEnglish[]Running60.02018-10-17NaTNaN15:00['Thursday']NaN80NaN60.0
307639441https://www.tvmaze.com/shows/39441/carl-webers-the-family-businessCarl Weber's The Family BusinessScriptedEnglish['Drama', 'Crime', 'Thriller']Running60.02018-11-13NaThttps://www.bet.plus/shows/carl-webers-the-family-business21:00[]5.095NaN59.0
307739441https://www.tvmaze.com/shows/39441/carl-webers-the-family-businessCarl Weber's The Family BusinessScriptedEnglish['Drama', 'Crime', 'Thriller']Running60.02018-11-13NaThttps://www.bet.plus/shows/carl-webers-the-family-business21:00[]5.095NaN59.0
307839441https://www.tvmaze.com/shows/39441/carl-webers-the-family-businessCarl Weber's The Family BusinessScriptedEnglish['Drama', 'Crime', 'Thriller']Running60.02018-11-13NaThttps://www.bet.plus/shows/carl-webers-the-family-business21:00[]5.095NaN59.0
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31852421https://www.tvmaze.com/shows/52421/you-complete-meYou Complete MeScriptedChinese['Drama', 'Romance']Ended45.02020-12-022021-01-14NaN20:00['Wednesday', 'Thursday', 'Friday']NaN2445.028
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33252524https://www.tvmaze.com/shows/52524/forever-loveForever LoveScriptedChinese['Drama', 'Romance']Ended45.02020-12-142021-01-05https://v.qq.com/detail/m/mzc00200dnvb1wh.html20:00['Monday', 'Tuesday', 'Wednesday']NaN3945.024